Specific Objectives of the Project:

1. Describe how nitrogen management practice adoption varies across farm size, crop types, and location. Analyze how grower behavior varies across fields and crop types.
2. Analyze whether there are diminishing marginal returns to nitrogen efficiency if multiple nutrient management practices are adopted.
3. Evaluate the effectiveness of practices designed to alter grower decision making on nitrogen application rates.
4. Determine a set of practices that growers find beneficial and have the strongest impact on nitrogen efficiency, to provide tangible recommendations to policymakers and technical advisers.

Summary of Research to Date:
Current results rely on data collected from the Irrigation and Nitrogen Management Plans that are required of agricultural producers in the Central Valley. The focus area is the East San Joaquin Valley (ESJV) in 2019, as this was the first region where data were required to be made publicly available. In the sample, there are 1713 growers applying nitrogen to 4810 fields.

We find in the ESJV, nitrogen application rates are highest for nut crops, with vegetable row crops not far behind. On average, vegetable row crops have the lowest nitrogen efficiency (calculated as N applied/N removed through biomass). The low nitrogen efficiency in vegetables is driven primarily by sweet potatoes. In total, split fertilizer applications, testing soil for residual nitrogen, and tissue testing are the most popular nutrient management practices adopted by growers. These results are similar to those found in surveys of growers in the Central Valley (Rudnick et al. 2021). The use of neutron probes, pressure bombs, and cover crops are the least likely to be adopted.

We regress nitrogen management practices adopted on nitrogen application rates, and nitrogen use efficiency, while accounting for irrigation system and crop and farmer fixed effects. We find that the adoption of cover crops reduces nitrogen application rates, but that several practices, including foliar nitrogen application, split fertilizer applications, tissue testing, soil testing, and pressure bombs, actually increase the nitrogen application rate. Tissue testing was significantly likely to improve nitrogen use efficiency, but use of soil testing and pressure bombs were associated with reduced efficiency.

Altogether, these results were a bit puzzling, and require a deeper exploration. We think there are two main challenges at play. The use of individual fixed effects, while they help combat omitted variable bias (farm and demographic characteristics), limit the variation in our current sample, especially since the majority of growers in the sample have a single field. We are in the process of securing more recent data to both expand the area of study to the full Central Valley, as well as the years of our sample, adding additional variation to the sample. Secondly, the number of practices in our data is likely leading to multicollinearity issues. We see significant evidence that farmers regularly adopt
“bundles” of practices in tandem, such as irrigation N tests and fertigation. To combat this issue, we use random forest techniques to isolate the variables that have the largest predictive power to explain nitrogen use, and reduce the number of practices in our model. The four main practices that have explanatory power include testing irrigation water for nitrogen, tissue testing, split nitrogen application, and foliar nitrogen application. We are also speaking with agronomists on alternate methods to group practices that are likely to be adopted together. We expect further insights and a concrete working paper to emerge with the application of these techniques to the expanded dataset.